results/orthofinder/output/Orthogroup_Sequences/OG0000000.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa
results/orthofinder/output/Orthogroup_Sequences/OG0000006.fa

results/orthofinder/orthosnap/OG0000005/OG0000005_orthosnap_0.fa

results/alignment/trimmed_cds/OG0000004.trimmed.cds.alignment.fa
results/alignment/trimmed_cds/OG0000000.trimmed.cds.alignment.fa

General Characteristics
=======================
6	Number of taxa
2388	Alignment length
496	Parsimony informative sites
496	Variable sites
1882	Constant sites

Character Frequencies
=====================
T	4524
G	2766
C	2251
A	4643
-	144

IQ-TREE 2.2.0.3 COVID-edition built Aug  2 2022

Input file name: results/supermatrix/supermatrix.cds.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 161615

REFERENCES
----------

To cite IQ-TREE please use:

Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015

To cite ModelFinder please use: 

Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285

Since you used ultrafast bootstrap (UFBoot) please also cite: 

Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281

SEQUENCE ALIGNMENT
------------------

Input data: 6 sequences with 2388 nucleotide sites
Number of constant sites: 1400 (= 58.6265% of all sites)
Number of invariant (constant or ambiguous constant) sites: 1400 (= 58.6265% of all sites)
Number of parsimony informative sites: 496
Number of distinct site patterns: 492

ModelFinder
-----------

Best-fit model according to BIC: GTR+F+G4

List of models sorted by BIC scores: 

Model                  LogL         AIC      w-AIC        AICc     w-AICc         BIC      w-BIC
GTR+F+G4          -9950.622   19937.245 +    0.254   19937.534 +    0.257   20041.253 +    0.464
TIM2+F+G4         -9958.889   19949.778 - 0.000483   19950.007 - 0.000503   20042.229 +    0.285
TIM2+F+I          -9960.089   19952.178 - 0.000145   19952.407 - 0.000152   20044.629 +   0.0857
GTR+F+I+G4        -9948.579   19935.159 +    0.721   19935.480 +    0.718   20044.945 +   0.0732
TIM2+F+I+G4       -9956.690   19947.381 -   0.0016   19947.639 -  0.00164   20045.610 +   0.0525
GTR+F+I           -9953.065   19942.130 -   0.0221   19942.419 -   0.0224   20046.138 -   0.0403
TPM2u+F+I         -9976.669   19983.337 - 2.49e-11   19983.540 - 2.63e-11   20070.011 - 2.64e-07
TVM+F+I           -9969.542   19973.084 -  4.2e-09   19973.343 - 4.31e-09   20071.314 - 1.38e-07
TVM+F+G4          -9970.077   19974.155 - 2.46e-09   19974.413 - 2.52e-09   20072.384 - 8.05e-08
TPM2u+F+G4        -9978.794   19987.588 - 2.97e-12   19987.790 - 3.14e-12   20074.261 - 3.15e-08
TVM+F+I+G4        -9968.124   19972.249 - 6.37e-09   19972.537 - 6.45e-09   20076.256 - 1.16e-08
TIM+F+G4          -9976.342   19984.683 - 1.27e-11   19984.913 - 1.32e-11   20077.135 - 7.49e-09
TPM2u+F+I+G4      -9976.786   19985.571 - 8.15e-12   19985.801 - 8.49e-12   20078.023 - 4.81e-09
TIM+F+I           -9978.268   19988.535 - 1.85e-12   19988.765 - 1.93e-12   20080.987 - 1.09e-09
TIM+F+I+G4        -9974.947   19983.894 - 1.89e-11   19984.152 - 1.94e-11   20082.123 - 6.18e-10
K3Pu+F+I          -9994.574   20019.149 - 4.17e-19   20019.351 -  4.4e-19   20105.822 - 4.42e-15
K3Pu+F+G4         -9997.747   20025.495 - 1.75e-20   20025.697 - 1.84e-20   20112.168 - 1.85e-16
K3Pu+F+I+G4       -9996.293   20024.586 - 2.75e-20   20024.816 - 2.87e-20   20117.038 - 1.62e-17
TN+F+G4          -10003.782   20037.564 - 4.18e-23   20037.767 - 4.41e-23   20124.237 - 4.43e-19
TN+F+I           -10005.761   20041.522 - 5.78e-24   20041.724 -  6.1e-24   20128.195 - 6.12e-20
TN+F+I+G4        -10002.313   20036.626 - 6.68e-23   20036.856 - 6.96e-23   20129.078 - 3.94e-20
TIM3+F+G4        -10003.134   20038.269 - 2.94e-23   20038.498 - 3.06e-23   20130.720 - 1.73e-20
TIM3+F+I         -10004.794   20041.588 - 5.59e-24   20041.817 - 5.83e-24   20134.039 -  3.3e-21
TIM3+F+I+G4      -10001.831   20037.662 - 3.98e-23   20037.920 - 4.09e-23   20135.892 - 1.31e-21
HKY+F+I          -10020.421   20068.842 - 6.75e-30   20069.019 - 7.22e-30   20149.737 - 1.29e-24
TPM3u+F+I        -10019.385   20068.770 -    7e-30   20068.972 - 7.39e-30   20155.443 - 7.41e-26
HKY+F+G4         -10023.678   20075.356 -  2.6e-31   20075.533 - 2.78e-31   20156.251 - 4.95e-26
HKY+F+I+G4       -10022.196   20074.393 - 4.21e-31   20074.595 - 4.44e-31   20161.066 - 4.46e-27
TPM3u+F+G4       -10022.668   20075.336 - 2.63e-31   20075.538 - 2.77e-31   20162.009 - 2.78e-27
TPM3u+F+I+G4     -10021.313   20074.626 - 3.74e-31   20074.855 -  3.9e-31   20167.077 - 2.21e-28
F81+F+I          -10041.407   20108.815 - 1.41e-38   20108.968 - 1.53e-38   20183.932 - 4.83e-32
F81+F+G4         -10046.374   20118.748 - 9.82e-41   20118.901 - 1.06e-40   20193.865 - 3.36e-34
F81+F+I+G4       -10044.860   20117.720 - 1.64e-40   20117.897 - 1.76e-40   20198.615 - 3.13e-35
SYM+I            -10106.619   20243.238 - 9.11e-68   20243.440 - 9.62e-68   20329.911 - 9.66e-64
TVMe+I           -10112.050   20252.100 - 1.08e-69   20252.277 - 1.16e-69   20332.995 - 2.07e-64
SYM+G4           -10109.264   20248.529 - 6.47e-69   20248.731 - 6.83e-69   20335.202 - 6.85e-65
SYM+I+G4         -10105.978   20243.957 - 6.36e-68   20244.186 - 6.63e-68   20336.408 - 3.75e-65
TVMe+G4          -10115.760   20259.521 - 2.65e-71   20259.698 - 2.84e-71   20340.416 - 5.06e-66
TVMe+I+G4        -10112.239   20254.478 -  3.3e-70   20254.680 - 3.49e-70   20341.151 -  3.5e-66
TIM2e+I          -10159.736   20345.473 - 5.75e-90   20345.626 - 6.22e-90   20420.589 - 1.97e-83
TPM2+I           -10164.413   20352.827 - 1.45e-91   20352.958 - 1.59e-91   20422.165 - 8.96e-84
TIM2e+I+G4       -10162.604   20353.209 -  1.2e-91   20353.386 - 1.29e-91   20434.104 - 2.29e-86
TIM2e+G4         -10166.496   20358.992 - 6.67e-93   20359.145 - 7.22e-93   20434.109 - 2.28e-86
TPM2+I+G4        -10167.174   20360.347 - 3.39e-93   20360.500 - 3.67e-93   20435.464 - 1.16e-86
TPM2+G4          -10171.100   20366.199 - 1.82e-94   20366.330 - 1.99e-94   20435.538 - 1.12e-86
TIMe+I           -10187.704   20401.408 - 4.11e-102   20401.562 - 4.44e-102   20476.525 - 1.41e-95
K3P+I            -10193.175   20410.349 - 4.7e-104   20410.480 - 5.14e-104   20479.688 - 2.89e-96
TIMe+G4          -10195.091   20416.182 - 2.54e-105   20416.335 - 2.75e-105   20491.298 - 8.71e-99
TIMe+I+G4        -10192.910   20413.821 - 8.28e-105   20413.998 - 8.86e-105   20494.716 - 1.58e-99
K3P+G4           -10203.234   20430.468 - 2.01e-108   20430.600 - 2.2e-108   20499.807 - 1.24e-100
K3P+I+G4         -10200.723   20427.446 - 9.11e-108   20427.600 - 9.86e-108   20502.563 - 3.12e-101
TIM3e+I          -10233.779   20493.557 - 4.01e-122   20493.711 - 4.34e-122   20568.674 - 1.37e-115
TIM3e+G4         -10234.984   20495.968 - 1.2e-122   20496.121 - 1.3e-122   20571.085 - 4.12e-116
TPM3+I           -10240.588   20505.176 - 1.2e-124   20505.307 - 1.32e-124   20574.515 - 7.41e-117
TIM3e+I+G4       -10233.164   20494.327 - 2.73e-122   20494.504 - 2.92e-122   20575.222 - 5.2e-117
TPM3+G4          -10246.096   20516.192 - 4.88e-127   20516.324 - 5.34e-127   20585.531 -   3e-119
TPM3+I+G4        -10243.636   20513.272 - 2.1e-126   20513.425 - 2.27e-126   20588.389 - 7.2e-120
GTR+F            -10231.559   20497.118 - 6.77e-123   20497.376 - 6.95e-123   20595.348 - 2.22e-121
TNe+I            -10266.205   20556.409 - 9.02e-136   20556.541 - 9.87e-136   20625.748 - 5.56e-128
JC+I             -10275.298   20570.596 - 7.49e-139   20570.689 - 8.36e-139   20628.378 - 1.49e-128
K2P+I            -10272.035   20566.071 - 7.2e-138   20566.182 - 7.96e-138   20629.631 - 7.97e-129
TNe+G4           -10273.280   20570.561 - 7.63e-139   20570.692 - 8.35e-139   20639.899 - 4.7e-131
TNe+I+G4         -10271.035   20568.070 - 2.65e-138   20568.223 - 2.87e-138   20643.186 - 9.08e-132
K2P+G4           -10281.744   20585.488 - 4.37e-142   20585.599 - 4.84e-142   20649.049 - 4.84e-133
JC+G4            -10285.852   20591.704 - 1.96e-143   20591.796 - 2.18e-143   20649.486 - 3.89e-133
K2P+I+G4         -10279.249   20582.498 - 1.95e-141   20582.630 - 2.14e-141   20651.837 - 1.2e-133
JC+I+G4          -10283.324   20588.649 - 9.01e-143   20588.760 - 9.96e-143   20652.209 - 9.97e-134

AIC, w-AIC   : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC   : Bayesian information criterion scores and weights.

Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.

SUBSTITUTION PROCESS
--------------------

Model of substitution: GTR+F+G4

Rate parameter R:

  A-C: 2.6505
  A-G: 2.9500
  A-T: 3.9453
  C-G: 1.9541
  C-T: 6.1752
  G-T: 1.0000

State frequencies: (empirical counts from alignment)

  pi(A) = 0.3273
  pi(C) = 0.1587
  pi(G) = 0.195
  pi(T) = 0.319

Rate matrix Q:

  A   -0.9608    0.1793    0.2452    0.5363
  C    0.3698    -1.372    0.1624    0.8394
  G    0.4116    0.1322   -0.6797    0.1359
  T    0.5504    0.4177   0.08311    -1.051

Model of rate heterogeneity: Gamma with 4 categories
Gamma shape alpha: 0.3949

 Category  Relative_rate  Proportion
  1         0.01598        0.25
  2         0.1779         0.25
  3         0.7257         0.25
  4         3.08           0.25
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.

MAXIMUM LIKELIHOOD TREE
-----------------------

Log-likelihood of the tree: -9950.6201 (s.e. 142.9199)
Unconstrained log-likelihood (without tree): -9284.4483
Number of free parameters (#branches + #model parameters): 18
Akaike information criterion (AIC) score: 19937.2401
Corrected Akaike information criterion (AICc) score: 19937.5288
Bayesian information criterion (BIC) score: 20041.2479

Total tree length (sum of branch lengths): 1.0856
Sum of internal branch lengths: 0.2051 (18.8890% of tree length)

NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are  ultrafast bootstrap support (%)

+-----------------------------------------------Avrainvillea_mazei_HV02664
|
|                +--------------------------------Bryopsis_plumosa_WEST4718
+----------------| (100)
|                +-----------------------------------------Derbesia_sp_WEST4838
|
|                  +--------------------------------------Caulerpa_cliftonii_HV03798
+------------------| (100)
                   |         +---------------------Chlorodesmis_fastigiata_HV03865
                   +---------| (98)
                             +----------------Flabellia_petiolata_HV01202

Tree in newick format:

(Avrainvillea_mazei_HV02664:0.2083416424,(Bryopsis_plumosa_WEST4718:0.1431662975,Derbesia_sp_WEST4838:0.1843593970)100:0.0749149176,(Caulerpa_cliftonii_HV03798:0.1700810002,(Chlorodesmis_fastigiata_HV03865:0.0968424550,Flabellia_petiolata_HV01202:0.0777150973)98:0.0437233286)100:0.0864127563);

CONSENSUS TREE
--------------

Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -9950.620052
Robinson-Foulds distance between ML tree and consensus tree: 0

Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)

+-----------------------------------------------Avrainvillea_mazei_HV02664
|
|                +--------------------------------Bryopsis_plumosa_WEST4718
+----------------| (100)
|                +-----------------------------------------Derbesia_sp_WEST4838
|
|                  +--------------------------------------Caulerpa_cliftonii_HV03798
+------------------| (100)
                   |         +---------------------Chlorodesmis_fastigiata_HV03865
                   +---------| (98)
                             +----------------Flabellia_petiolata_HV01202


Consensus tree in newick format: 

(Avrainvillea_mazei_HV02664:0.2083451933,(Bryopsis_plumosa_WEST4718:0.1431632302,Derbesia_sp_WEST4838:0.1843631097)100:0.0749217475,(Caulerpa_cliftonii_HV03798:0.1701007202,(Chlorodesmis_fastigiata_HV03865:0.0968536522,Flabellia_petiolata_HV01202:0.0777184217)98:0.0437063646)100:0.0864044165);

TIME STAMP
----------

Date and time: Sat Dec 16 03:48:38 2023
Total CPU time used: 3534.348888 seconds (0h:58m:54s)
Total wall-clock time used: 138.9548088 seconds (0h:2m:18s)

IQ-TREE multicore version 2.2.0.3 COVID-edition for Linux 64-bit built Aug  2 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.

Host:    herbairumphylo (AVX512, FMA3, 283 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.cds.fa -bb 1000 -m TEST -nt 32 -redo
Seed:    161615 (Using SPRNG - Scalable Parallel Random Number Generator)
Time:    Sat Dec 16 03:42:46 2023
Kernel:  AVX+FMA - 32 threads (32 CPU cores detected)

Reading alignment file results/supermatrix/supermatrix.cds.fa ... Fasta format detected
Reading fasta file: done in 0.000622578 secs
Alignment most likely contains DNA/RNA sequences
Constructing alignment: done in 0.00448822 secs using 867.2% CPU
Alignment has 6 sequences with 2388 columns, 492 distinct patterns
496 parsimony-informative, 492 singleton sites, 1399 constant sites
                                 Gap/Ambiguity  Composition  p-value
Analyzing sequences: done in 0.020126 secs using 579.1% CPU
   1  Avrainvillea_mazei_HV02664         1.13%    passed     34.31%
   2  Bryopsis_plumosa_WEST4718          0.38%    passed     73.08%
   3  Caulerpa_cliftonii_HV03798         0.00%    passed     67.19%
   4  Chlorodesmis_fastigiata_HV03865    1.51%    passed     91.03%
   5  Derbesia_sp_WEST4838               0.63%    passed     90.09%
   6  Flabellia_petiolata_HV01202        2.39%    passed     90.54%
****  TOTAL                              1.01%  0 sequences failed composition chi2 test (p-value<5%; df=3)
Checking for duplicate sequences: done in 0.0198425 secs using 584.8% CPU


Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.004 seconds
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Perform fast likelihood tree search using GTR+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 1.81597 secs using 816.4% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -9948.592
Optimal log-likelihood: -9948.559
Rate parameters:  A-C: 2.77621  A-G: 3.02265  A-T: 4.05715  C-G: 2.02910  C-T: 6.35200  G-T: 1.00000
Base frequencies:  A: 0.327  C: 0.159  G: 0.195  T: 0.319
Proportion of invariable sites: 0.294
Gamma shape alpha: 0.898
Parameters optimization took 1 rounds (1.082 sec)
Time for fast ML tree search: 11.582 seconds

NOTE: ModelFinder requires 2 MB RAM!
ModelFinder will test up to 88 DNA models (sample size: 2388) ...
 No. Model         -LnL         df  AIC          AICc         BIC
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  1  GTR+F         10231.559    17  20497.118    20497.376    20595.348
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  2  GTR+F+I       9953.065     18  19942.130    19942.419    20046.138
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  3  GTR+F+G4      9950.622     18  19937.245    19937.534    20041.253
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  4  GTR+F+I+G4    9948.579     19  19935.159    19935.480    20044.945
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  6  SYM+I         10106.619    15  20243.238    20243.440    20329.911
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  7  SYM+G4        10109.264    15  20248.529    20248.731    20335.202
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
  8  SYM+I+G4      10105.978    16  20243.957    20244.186    20336.408
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 10  TVM+F+I       9969.542     17  19973.084    19973.343    20071.314
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 11  TVM+F+G4      9970.077     17  19974.155    19974.413    20072.384
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 12  TVM+F+I+G4    9968.124     18  19972.249    19972.537    20076.256
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 14  TVMe+I        10112.050    14  20252.100    20252.277    20332.995
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 15  TVMe+G4       10115.760    14  20259.521    20259.698    20340.416
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 16  TVMe+I+G4     10112.239    15  20254.478    20254.680    20341.151
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 18  TIM3+F+I      10004.794    16  20041.588    20041.817    20134.039
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 19  TIM3+F+G4     10003.134    16  20038.269    20038.498    20130.720
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 20  TIM3+F+I+G4   10001.831    17  20037.662    20037.920    20135.892
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 22  TIM3e+I       10233.779    13  20493.557    20493.711    20568.674
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 23  TIM3e+G4      10234.984    13  20495.968    20496.121    20571.085
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 24  TIM3e+I+G4    10233.164    14  20494.327    20494.504    20575.222
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 26  TIM2+F+I      9960.089     16  19952.178    19952.407    20044.629
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 27  TIM2+F+G4     9958.889     16  19949.778    19950.007    20042.229
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 28  TIM2+F+I+G4   9956.690     17  19947.381    19947.639    20045.610
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 30  TIM2e+I       10159.736    13  20345.473    20345.626    20420.589
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 31  TIM2e+G4      10166.496    13  20358.992    20359.145    20434.109
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 32  TIM2e+I+G4    10162.604    14  20353.209    20353.386    20434.104
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 34  TIM+F+I       9978.268     16  19988.535    19988.765    20080.987
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 35  TIM+F+G4      9976.342     16  19984.683    19984.913    20077.135
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 36  TIM+F+I+G4    9974.947     17  19983.894    19984.152    20082.123
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 38  TIMe+I        10187.704    13  20401.408    20401.562    20476.525
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 39  TIMe+G4       10195.091    13  20416.182    20416.335    20491.298
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 40  TIMe+I+G4     10192.910    14  20413.821    20413.998    20494.716
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 42  TPM3u+F+I     10019.385    15  20068.770    20068.972    20155.443
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 43  TPM3u+F+G4    10022.668    15  20075.336    20075.538    20162.009
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 44  TPM3u+F+I+G4  10021.313    16  20074.626    20074.855    20167.077
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 46  TPM3+I        10240.588    12  20505.176    20505.307    20574.515
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 47  TPM3+G4       10246.096    12  20516.192    20516.324    20585.531
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 48  TPM3+I+G4     10243.636    13  20513.272    20513.425    20588.389
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 50  TPM2u+F+I     9976.669     15  19983.337    19983.540    20070.011
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 51  TPM2u+F+G4    9978.794     15  19987.588    19987.790    20074.261
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 52  TPM2u+F+I+G4  9976.786     16  19985.571    19985.801    20078.023
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 54  TPM2+I        10164.413    12  20352.827    20352.958    20422.165
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 55  TPM2+G4       10171.099    12  20366.199    20366.330    20435.538
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 56  TPM2+I+G4     10167.174    13  20360.347    20360.500    20435.464
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 58  K3Pu+F+I      9994.574     15  20019.149    20019.351    20105.822
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 59  K3Pu+F+G4     9997.747     15  20025.495    20025.697    20112.168
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 60  K3Pu+F+I+G4   9996.293     16  20024.586    20024.816    20117.038
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 62  K3P+I         10193.175    12  20410.349    20410.480    20479.688
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 63  K3P+G4        10203.234    12  20430.468    20430.600    20499.807
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 64  K3P+I+G4      10200.723    13  20427.446    20427.600    20502.563
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 66  TN+F+I        10005.761    15  20041.522    20041.724    20128.195
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 67  TN+F+G4       10003.782    15  20037.564    20037.767    20124.237
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 68  TN+F+I+G4     10002.313    16  20036.626    20036.856    20129.078
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 70  TNe+I         10266.205    12  20556.409    20556.541    20625.748
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 71  TNe+G4        10273.280    12  20570.561    20570.692    20639.899
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 72  TNe+I+G4      10271.035    13  20568.070    20568.223    20643.186
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 74  HKY+F+I       10020.421    14  20068.842    20069.019    20149.737
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 75  HKY+F+G4      10023.678    14  20075.356    20075.533    20156.251
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 76  HKY+F+I+G4    10022.196    15  20074.393    20074.595    20161.066
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 78  K2P+I         10272.035    11  20566.071    20566.182    20629.631
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 79  K2P+G4        10281.744    11  20585.488    20585.599    20649.049
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 80  K2P+I+G4      10279.249    12  20582.498    20582.630    20651.837
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 82  F81+F+I       10041.407    13  20108.815    20108.968    20183.932
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 83  F81+F+G4      10046.374    13  20118.748    20118.901    20193.865
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 84  F81+F+I+G4    10044.860    14  20117.720    20117.897    20198.615
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 86  JC+I          10275.298    10  20570.596    20570.689    20628.378
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 87  JC+G4         10285.852    10  20591.704    20591.796    20649.486
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
 88  JC+I+G4       10283.324    11  20588.649    20588.760    20652.209
Akaike Information Criterion:           GTR+F+I+G4
Corrected Akaike Information Criterion: GTR+F+I+G4
Bayesian Information Criterion:         GTR+F+G4
Best-fit model: GTR+F+G4 chosen according to BIC

All model information printed to results/supermatrix/supermatrix.cds.fa.model.gz
CPU time for ModelFinder: 2560.821 seconds (0h:42m:40s)
Wall-clock time for ModelFinder: 213.459 seconds (0h:3m:33s)
Generating 1000 samples for ultrafast bootstrap (seed: 161615)...

NOTE: 2 MB RAM (0 GB) is required!
WARNING: Number of threads seems too high for short alignments. Use -T AUTO to determine best number of threads.
Estimate model parameters (epsilon = 0.100)
1. Initial log-likelihood: -9950.622
Optimal log-likelihood: -9950.621
Rate parameters:  A-C: 2.64999  A-G: 2.93589  A-T: 3.95010  C-G: 1.95186  C-T: 6.16638  G-T: 1.00000
Base frequencies:  A: 0.327  C: 0.159  G: 0.195  T: 0.319
Gamma shape alpha: 0.395
Parameters optimization took 1 rounds (0.701 sec)
Wrote distance file to... 
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00680771 secs using 1641% CPU
Computing ML distances took 0.038560 sec (of wall-clock time) 0.407615 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 0.0125567 secs using 125.4% CPU
Constructing RapidNJ tree: done in 0.0326775 secs
Computing RapidNJ tree took 0.045494 sec (of wall-clock time) 0.019727 sec (of CPU time)
Log-likelihood of RapidNJ tree: -9985.567
--------------------------------------------------------------------
|             INITIALIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Generating 98 parsimony trees... 0.036 second
Computing log-likelihood of 5 initial trees ... 1.796 seconds
Current best score: -9950.621

Do NNI search on 7 best initial trees
Optimizing NNI: done in 1.07794 secs using 1320% CPU
Optimizing NNI: done in 4.34915 secs using 823.7% CPU
Optimizing NNI: done in 1.9475 secs using 1719% CPU
Optimizing NNI: done in 1.38306 secs using 1540% CPU
Optimizing NNI: done in 0.128873 secs using 1496% CPU
Optimizing NNI: done in 2.7676 secs using 1134% CPU
Optimizing NNI: done in 1.61927 secs using 2118% CPU
Finish initializing candidate tree set (7)
Current best tree score: -9950.621 / CPU time: 15.424
Number of iterations: 7
--------------------------------------------------------------------
|               OPTIMIZING CANDIDATE TREE SET                      |
--------------------------------------------------------------------
Optimizing NNI: done in 1.2637 secs using 2331% CPU
Optimizing NNI: done in 1.25443 secs using 2252% CPU
Optimizing NNI: done in 1.24538 secs using 2669% CPU
Iteration 10 / LogL: -9950.844 / Time: 0h:0m:21s
Optimizing NNI: done in 1.29607 secs using 2272% CPU
Optimizing NNI: done in 0.51465 secs using 2616% CPU
Optimizing NNI: done in 1.12706 secs using 2781% CPU
UPDATE BEST LOG-LIKELIHOOD: -9950.621
Optimizing NNI: done in 1.22644 secs using 2840% CPU
Optimizing NNI: done in 2.03725 secs using 2933% CPU
Optimizing NNI: done in 1.23451 secs using 2816% CPU
Optimizing NNI: done in 1.26577 secs using 2534% CPU
Optimizing NNI: done in 2.00597 secs using 2854% CPU
Optimizing NNI: done in 0.1121 secs using 2942% CPU
Optimizing NNI: done in 1.94325 secs using 2761% CPU
Iteration 20 / LogL: -9950.638 / Time: 0h:0m:34s
Optimizing NNI: done in 1.25501 secs using 2649% CPU
Optimizing NNI: done in 1.10358 secs using 2628% CPU
UPDATE BEST LOG-LIKELIHOOD: -9950.621
Optimizing NNI: done in 1.24876 secs using 2637% CPU
Optimizing NNI: done in 1.07974 secs using 2687% CPU
Optimizing NNI: done in 1.06359 secs using 2647% CPU
Optimizing NNI: done in 1.09765 secs using 2338% CPU
UPDATE BEST LOG-LIKELIHOOD: -9950.621
Optimizing NNI: done in 1.92631 secs using 2718% CPU
Optimizing NNI: done in 1.2097 secs using 2955% CPU
Optimizing NNI: done in 1.16211 secs using 2921% CPU
Optimizing NNI: done in 0.495309 secs using 2979% CPU
Iteration 30 / LogL: -9960.717 / Time: 0h:0m:45s (0h:1m:50s left)
Optimizing NNI: done in 0.505813 secs using 2938% CPU
Optimizing NNI: done in 1.90804 secs using 2650% CPU
Optimizing NNI: done in 1.16779 secs using 2573% CPU
Optimizing NNI: done in 1.94331 secs using 2939% CPU
Optimizing NNI: done in 1.60375 secs using 2984% CPU
Optimizing NNI: done in 0.495241 secs using 2997% CPU
Optimizing NNI: done in 1.06242 secs using 2898% CPU
Optimizing NNI: done in 1.25887 secs using 2648% CPU
Optimizing NNI: done in 1.2501 secs using 2670% CPU
Optimizing NNI: done in 1.19824 secs using 2672% CPU
Iteration 40 / LogL: -9950.646 / Time: 0h:0m:58s (0h:1m:30s left)
Optimizing NNI: done in 1.92262 secs using 2556% CPU
Optimizing NNI: done in 1.2263 secs using 2404% CPU
Optimizing NNI: done in 1.1602 secs using 2967% CPU
Optimizing NNI: done in 1.16783 secs using 2928% CPU
Optimizing NNI: done in 0.566042 secs using 2926% CPU
Optimizing NNI: done in 1.79761 secs using 2936% CPU
Optimizing NNI: done in 0.213822 secs using 3031% CPU
Optimizing NNI: done in 1.93768 secs using 2473% CPU
Optimizing NNI: done in 1.20909 secs using 2882% CPU
Optimizing NNI: done in 1.12051 secs using 2918% CPU
Iteration 50 / LogL: -9950.629 / Time: 0h:1m:11s (0h:1m:12s left)
Log-likelihood cutoff on original alignment: -9981.047
Optimizing NNI: done in 0.842645 secs using 2953% CPU
Optimizing NNI: done in 1.17608 secs using 2930% CPU
Optimizing NNI: done in 1.11613 secs using 2955% CPU
Optimizing NNI: done in 1.21222 secs using 2674% CPU
Optimizing NNI: done in 1.05147 secs using 2679% CPU
Optimizing NNI: done in 1.94104 secs using 2686% CPU
Optimizing NNI: done in 2.08926 secs using 2435% CPU
Optimizing NNI: done in 1.18808 secs using 2650% CPU
Optimizing NNI: done in 0.493154 secs using 2967% CPU
Optimizing NNI: done in 1.17491 secs using 2909% CPU
Iteration 60 / LogL: -9950.646 / Time: 0h:1m:23s (0h:0m:56s left)
Optimizing NNI: done in 0.351238 secs using 2965% CPU
Optimizing NNI: done in 1.94391 secs using 2932% CPU
Optimizing NNI: done in 1.23189 secs using 2708% CPU
Optimizing NNI: done in 2.09152 secs using 2586% CPU
Optimizing NNI: done in 1.89626 secs using 2939% CPU
Optimizing NNI: done in 1.16286 secs using 2946% CPU
Optimizing NNI: done in 1.15525 secs using 2959% CPU
Optimizing NNI: done in 0.520634 secs using 2725% CPU
Optimizing NNI: done in 1.21722 secs using 2623% CPU
Optimizing NNI: done in 1.95423 secs using 2658% CPU
Iteration 70 / LogL: -9950.638 / Time: 0h:1m:37s (0h:0m:42s left)
Optimizing NNI: done in 1.08009 secs using 2667% CPU
Optimizing NNI: done in 2.00654 secs using 2387% CPU
Optimizing NNI: done in 1.91199 secs using 2851% CPU
Optimizing NNI: done in 0.491389 secs using 2974% CPU
Optimizing NNI: done in 1.20929 secs using 2941% CPU
Optimizing NNI: done in 1.20065 secs using 2973% CPU
Optimizing NNI: done in 0.577667 secs using 2944% CPU
Optimizing NNI: done in 2.11667 secs using 2434% CPU
Optimizing NNI: done in 1.8466 secs using 2956% CPU
Optimizing NNI: done in 0.488527 secs using 2922% CPU
Iteration 80 / LogL: -9971.306 / Time: 0h:1m:50s (0h:0m:27s left)
Optimizing NNI: done in 1.1698 secs using 2945% CPU
Optimizing NNI: done in 1.21815 secs using 2959% CPU
Optimizing NNI: done in 0.50812 secs using 2877% CPU
Optimizing NNI: done in 1.19092 secs using 2691% CPU
Optimizing NNI: done in 0.51549 secs using 2688% CPU
Optimizing NNI: done in 2.04443 secs using 2661% CPU
Optimizing NNI: done in 1.22694 secs using 2674% CPU
Optimizing NNI: done in 1.93917 secs using 2382% CPU
Optimizing NNI: done in 1.22256 secs using 2937% CPU
Optimizing NNI: done in 1.63885 secs using 2948% CPU
Iteration 90 / LogL: -9950.971 / Time: 0h:2m:3s (0h:0m:13s left)
Optimizing NNI: done in 0.471577 secs using 3040% CPU
Optimizing NNI: done in 1.8703 secs using 2962% CPU
Optimizing NNI: done in 1.31571 secs using 2207% CPU
Optimizing NNI: done in 0.774541 secs using 2930% CPU
Optimizing NNI: done in 1.19238 secs using 2977% CPU
Optimizing NNI: done in 1.15221 secs using 2891% CPU
Optimizing NNI: done in 1.85709 secs using 2969% CPU
Optimizing NNI: done in 1.2023 secs using 2789% CPU
Optimizing NNI: done in 1.20508 secs using 2657% CPU
Optimizing NNI: done in 1.20611 secs using 2659% CPU
Iteration 100 / LogL: -9950.648 / Time: 0h:2m:16s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -9981.047
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 1.96446 secs using 2679% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:2m:18s

--------------------------------------------------------------------
|                    FINALIZING TREE SEARCH                        |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -9950.621
Optimal log-likelihood: -9950.620
Rate parameters:  A-C: 2.65045  A-G: 2.95002  A-T: 3.94530  C-G: 1.95408  C-T: 6.17521  G-T: 1.00000
Base frequencies:  A: 0.327  C: 0.159  G: 0.195  T: 0.319
Gamma shape alpha: 0.395
Parameters optimization took 1 rounds (0.317 sec)
BEST SCORE FOUND : -9950.620
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.cds.fa.splits.nex
Total tree length: 1.086

Total number of iterations: 101
CPU time used for tree search: 3503.100 sec (0h:58m:23s)
Wall-clock time used for tree search: 136.261 sec (0h:2m:16s)
Total CPU time used: 3534.349 sec (0h:58m:54s)
Total wall-clock time used: 138.369 sec (0h:2m:18s)

Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.cds.fa.splits.nex...
6 taxa and 11 splits.
Consensus tree written to results/supermatrix/supermatrix.cds.fa.contree
Reading input trees file results/supermatrix/supermatrix.cds.fa.contree
Log-likelihood of consensus tree: -9950.620

Analysis results written to: 
  IQ-TREE report:                results/supermatrix/supermatrix.cds.fa.iqtree
  Maximum-likelihood tree:       results/supermatrix/supermatrix.cds.fa.treefile
  Likelihood distances:          results/supermatrix/supermatrix.cds.fa.mldist

Ultrafast bootstrap approximation results written to:
  Split support values:          results/supermatrix/supermatrix.cds.fa.splits.nex
  Consensus tree:                results/supermatrix/supermatrix.cds.fa.contree
  Screen log file:               results/supermatrix/supermatrix.cds.fa.log

ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.cds.fa.treefile -m "GTR{2.65045,2.95002,3.9453,1.95408,6.17521}+F{0.327341,0.1587,0.195008,0.318951}+G4{0.394866}" --length 2388

Date and Time: Sat Dec 16 03:48:38 2023

@article{10.1093/molbev/mst010,
    author = "Katoh, Kazutaka and Standley, Daron M.",
    title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
    journal = "Molecular Biology and Evolution",
    volume = "30",
    number = "4",
    pages = "772-780",
    year = "2013",
    month = "01",
    abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/mst010",
    url = "https://doi.org/10.1093/molbev/mst010",
    eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}

@article{Emms2019,
    author = "Emms, David M. and Kelly, Steven",
    type = "Journal Article",
    title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
    journal = "Genome Biology",
    number = "1",
    doi = "10.1186/s13059-019-1832-y",
    volume = "20",
    pages = "238",
    url = "https://doi.org/10.1186/s13059-019-1832-y",
    year = "2019",
    abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
    isbn = "1474-760X",
    DA = "2019/11/14"
}

@article{10.1371/journal.pbio.3001007,
    author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    doi = "10.1371/journal.pbio.3001007",
    journal = "PLOS Biology",
    publisher = "Public Library of Science",
    title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
    year = "2020",
    month = "12",
    volume = "18",
    url = "https://doi.org/10.1371/journal.pbio.3001007",
    pages = "1-17",
    abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
    number = "12"
}

@article{Steenwyk_2021,
    author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
    doi = "10.1101/2021.10.02.462868",
    url = "https://doi.org/10.1101\%2F2021.10.02.462868",
    year = "2021",
    month = "oct",
    journal = "biorxiv",
    publisher = "Cold Spring Harbor Laboratory",
    title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}

@article{10.1093/molbev/msx281,
    author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
    title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
    journal = "Molecular Biology and Evolution",
    volume = "35",
    number = "2",
    pages = "518-522",
    year = "2017",
    month = "10",
    abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msx281",
    url = "https://doi.org/10.1093/molbev/msx281",
    eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}

@article{10.1093/bioinformatics/btab096,
    author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
    title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
    journal = "Bioinformatics",
    volume = "37",
    number = "16",
    pages = "2325-2331",
    year = "2021",
    month = "02",
    abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
    issn = "1367-4803",
    doi = "10.1093/bioinformatics/btab096",
    url = "https://doi.org/10.1093/bioinformatics/btab096",
    eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}

@article{eaton_toytree_2020,
    author = "Eaton, Deren A. R.",
    title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
    volume = "11",
    doi = "10.1111/2041-210X.13313",
    journal = "Methods in Ecology and Evolution",
    year = "2020",
    pages = "187--191"
}

@article{10.1093/molbev/msaa015,
    author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
    title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
    journal = "Molecular Biology and Evolution",
    volume = "37",
    number = "5",
    pages = "1530-1534",
    year = "2020",
    month = "02",
    abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
    issn = "0737-4038",
    doi = "10.1093/molbev/msaa015",
    url = "https://doi.org/10.1093/molbev/msaa015",
    eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}

@article{Kalyaanamoorthy2017,
    author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
    type = "Journal Article",
    title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
    journal = "Nature Methods",
    number = "6",
    doi = "10.1038/nmeth.4285",
    volume = "14",
    pages = "587--589",
    url = "https://doi.org/10.1038/nmeth.4285",
    year = "2017",
    abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
    issn = "1548-7105",
    DA = "2017/06/01"
}

@article{Zhang2018,
    author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
    type = "Journal Article",
    title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
    journal = "BMC Bioinformatics",
    number = "6",
    doi = "10.1186/s12859-018-2129-y",
    volume = "19",
    pages = "153",
    url = "https://doi.org/10.1186/s12859-018-2129-y",
    year = "2018",
    abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
    issn = "1471-2105",
    DA = "2018/05/08"
}